| Autor: |
Ginting, Muhammad Fadhil, Kim, Sung-Kyun, Peltzer, Oriana, Ott, Joshua, Jung, Sunggoo, Kochenderfer, Mykel J., Agha-mohammadi, Ali-akbar |
| Rok vydání: |
2023 |
| Předmět: |
|
| DOI: |
10.48550/arxiv.2304.00645 |
| Popis: |
To achieve autonomy in unknown and unstructured environments, we propose a method for semantic-based planning under perceptual uncertainty. This capability is crucial for safe and efficient robot navigation in environment with mobility-stressing elements that require terrain-specific locomotion policies. We propose the Semantic Belief Graph (SBG), a geometric- and semantic-based representation of a robot's probabilistic roadmap in the environment. The SBG nodes comprise of the robot geometric state and the semantic-knowledge of the terrains in the environment. The SBG edges represent local semantic-based controllers that drive the robot between the nodes or invoke an information gathering action to reduce semantic belief uncertainty. We formulate a semantic-based planning problem on SBG that produces a policy for the robot to safely navigate to the target location with minimal traversal time. We analyze our method in simulation and present real-world results with a legged robotic platform navigating multi-level outdoor environments. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
